Changes for page Requirements

Last modified by Robert Schaub on 2026/02/08 21:32

From 4.1 to 3.1 From 7.2 to 7.1

From version 7.1

edited by Robert Schaub
on 2025/12/24 21:53

Change comment: Imported from XAR

To version 4.1

edited by Robert Schaub
on 2025/12/19 10:02

Change comment: There is no comment for this version

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@@ -1,9 +1,5 @@
  = Requirements =
--{{info}}
--**Phase Assignments:** See [[Requirements Roadmap Matrix>>FactHarbor.Roadmap.Requirements-Roadmap-Matrix.WebHome]] for which requirements are implemented in which phases.
--{{/info}}
--
  **This page defines Roles, Content States, Rules, and System Requirements for FactHarbor.**
  **Core Philosophy:** Invest in system improvement, not manual data correction. When AI makes errors, improve the algorithm and re-process automatically.
@@ -46,7 +46,7 @@
  * Modify content
  * Access edit history details
--**User Needs served**: UN-1 (Trust assessment), UN-2 (Claim verification), UN-3 (Article summary with FactHarbor analysis summary), UN-4 (Social media fact-checking), UN-5 (Source tracing), UN-7 (Evidence transparency), UN-8 (Understanding disagreement), UN-12 (Submit claims), UN-17 (In-article highlighting)
++**User Needs served**: UN-1 (Trust assessment), UN-2 (Claim verification), UN-3 (Article summary with FactHarbor analysis summary), UN-4 (Social media fact-checking), UN-5 (Source tracing), UN-7 (Evidence transparency), UN-8 (Understanding disagreement), UN-12 (Submit claims)
  === 1.2 Contributor ===
@@ -143,7 +143,7 @@
  * High controversy: "Disputed - multiple interpretations exist"
  * Medical/Legal/Safety domain: "Seek professional advice"
--**User Needs served**: UN-1 (Trust score), UN-9 (Methodology transparency), ~~UN-15 (Evolution timeline - Deferred)~~, UN-16 (Review status)
++**User Needs served**: UN-1 (Trust score), UN-9 (Methodology transparency), UN-15 (Evolution timeline), UN-16 (Review status)
  === 2.2 Hidden ===
@@ -310,7 +310,7 @@
 . How common is this pattern?
 . Store in ErrorPattern table (improvement queue)
--=== 6.2 Continuous Improvement Cycle ===
++=== 6.2 Weekly Improvement Cycle ===
 . **Review**: Analyze top error patterns
 . **Develop**: Create fix (prompt, model, validation)
@@ -330,7 +330,7 @@
  * Re-work rate
  * Claims processed per hour
--**Goal**: continuous improvement in error rate
++**Goal**: 10% monthly improvement in error rate
  == 7. Automated Quality Monitoring ==
@@ -442,12 +442,6 @@
  ==== FR8 — Time Evolution ====
--{{warning}}
--**Status:** Deferred (Not in V1.0)
--
--This requirement has been **dropped from the current architecture and design**. Versioned entities have been replaced with simple edit history tracking only. Full evolution timeline functionality is deferred to future releases beyond V1.0.
--{{/warning}}
--
  **Fulfills**: UN-15 (Verdict evolution timeline)
  * Claims and verdicts update as new evidence emerges
@@ -491,168 +491,6 @@
  * Exportable as standalone summary report
  * Shareable via permanent URL
--==== FR13 — In-Article Claim Highlighting ====
--
--**Fulfills**: UN-17 (In-article claim highlighting)
--
--**Purpose**: Enable readers to quickly assess claim credibility while reading by visually highlighting factual claims with color-coded indicators
--
--==== Visual Example: Article with Highlighted Claims ====
--
--(% class="box" %)
--(((
--**Article: "New Study Shows Benefits of Mediterranean Diet"**
--
--A recent study published in the Journal of Nutrition has revealed new findings about the Mediterranean diet.
--
--(% class="box successmessage" style="margin:10px 0;" %)
--(((
--🟢 **Researchers found that Mediterranean diet followers had a 25% lower risk of heart disease compared to control groups**
--
--(% style="font-size:0.9em; color:#666;" %)
--↑ WELL SUPPORTED • 87% confidence
--[[Click for evidence details →]]
--(%%)
--)))
--
--The study, which followed 10,000 participants over five years, showed significant improvements in cardiovascular health markers.
--
--(% class="box warningmessage" style="margin:10px 0;" %)
--(((
--🟡 **Some experts believe this diet can completely prevent heart attacks**
--
--(% style="font-size:0.9em; color:#666;" %)
--↑ UNCERTAIN • 45% confidence
--Overstated - evidence shows risk reduction, not prevention
--[[Click for details →]]
--(%%)
--)))
--
--Dr. Maria Rodriguez, lead researcher, recommends incorporating more olive oil, fish, and vegetables into daily meals.
--
--(% class="box errormessage" style="margin:10px 0;" %)
--(((
--🔴 **The study proves that saturated fats cause heart disease**
--
--(% style="font-size:0.9em; color:#666;" %)
--↑ REFUTED • 15% confidence
--Claim not supported by study design; correlation ≠ causation
--[[Click for counter-evidence →]]
--(%%)
--)))
--
--Participants also reported feeling more energetic and experiencing better sleep quality, though these were secondary measures.
--)))
--
--**Legend:**
--* 🟢 = Well-supported claim (confidence ≥75%)
--* 🟡 = Uncertain claim (confidence 40-74%)
--* 🔴 = Refuted/unsupported claim (confidence <40%)
--* Plain text = Non-factual content (context, opinions, recommendations)
--
--==== Tooltip on Hover/Click ====
--
--(% class="box infomessage" %)
--(((
--**FactHarbor Analysis**
--
--**Claim:**
--"Researchers found that Mediterranean diet followers had a 25% lower risk of heart disease"
--
--**Verdict:** WELL SUPPORTED
--**Confidence:** 87%
--
--**Evidence Summary:**
--* Meta-analysis of 12 RCTs confirms 23-28% risk reduction
--* Consistent findings across multiple populations
--* Published in peer-reviewed journal (high credibility)
--
--**Uncertainty Factors:**
--* Exact percentage varies by study (20-30% range)
--
--[[View Full Analysis →]]
--)))
--
--**Color-Coding System**:
--* **Green**: Well-supported claims (confidence ≥75%, strong evidence)
--* **Yellow/Orange**: Uncertain claims (confidence 40-74%, conflicting or limited evidence)
--* **Red**: Refuted or unsupported claims (confidence <40%, contradicted by evidence)
--* **Gray/Neutral**: Non-factual content (opinions, questions, procedural text)
--
--==== Interactive Highlighting Example (Detailed View) ====
--
--(% style="width:100%; border-collapse:collapse;" %)
--|=**Article Text**|=**Status**|=**Analysis**
--|(((A recent study published in the Journal of Nutrition has revealed new findings about the Mediterranean diet.)))|(% style="text-align:center;" %)Plain text|(% style="font-style:italic; color:#888;" %)Context - no highlighting
--|(((//Researchers found that Mediterranean diet followers had a 25% lower risk of heart disease compared to control groups//)))|(% style="background-color:#D4EDDA; text-align:center; padding:8px;" %)🟢 **WELL SUPPORTED**|(((
--**87% confidence**
--
--Meta-analysis of 12 RCTs confirms 23-28% risk reduction
--
--[[View Full Analysis]]
--)))
--|(((The study, which followed 10,000 participants over five years, showed significant improvements in cardiovascular health markers.)))|(% style="text-align:center;" %)Plain text|(% style="font-style:italic; color:#888;" %)Methodology - no highlighting
--|(((//Some experts believe this diet can completely prevent heart attacks//)))|(% style="background-color:#FFF3CD; text-align:center; padding:8px;" %)🟡 **UNCERTAIN**|(((
--**45% confidence**
--
--Overstated - evidence shows risk reduction, not prevention
--
--[[View Details]]
--)))
--|(((Dr. Rodriguez recommends incorporating more olive oil, fish, and vegetables into daily meals.)))|(% style="text-align:center;" %)Plain text|(% style="font-style:italic; color:#888;" %)Recommendation - no highlighting
--|(((//The study proves that saturated fats cause heart disease//)))|(% style="background-color:#F8D7DA; text-align:center; padding:8px;" %)🔴 **REFUTED**|(((
--**15% confidence**
--
--Claim not supported by study; correlation ≠ causation
--
--[[View Counter-Evidence]]
--)))
--
--**Design Notes:**
--* Highlighted claims use italics to distinguish from plain text
--* Color backgrounds match XWiki message box colors (success/warning/error)
--* Status column shows verdict prominently
--* Analysis column provides quick summary with link to details
--
--**User Actions**:
--* **Hover** over highlighted claim → Tooltip appears
--* **Click** highlighted claim → Detailed analysis modal/panel
--* **Toggle** button to turn highlighting on/off
--* **Keyboard**: Tab through highlighted claims
--
--**Interaction Design**:
--* Hover/click on highlighted claim → Show tooltip with:
-- * Claim text
-- * Verdict (e.g., "WELL SUPPORTED")
-- * Confidence score (e.g., "85%")
-- * Brief evidence summary
-- * Link to detailed analysis
--* Toggle highlighting on/off (user preference)
--* Adjustable color intensity for accessibility
--
--**Technical Requirements**:
--* Real-time highlighting as page loads (non-blocking)
--* Claim boundary detection (start/end of assertion)
--* Handle nested or overlapping claims
--* Preserve original article formatting
--* Work with various content formats (HTML, plain text, PDFs)
--
--**Performance Requirements**:
--* Highlighting renders within 500ms of page load
--* No perceptible delay in reading experience
--* Efficient DOM manipulation (avoid reflows)
--
--**Accessibility**:
--* Color-blind friendly palette (use patterns/icons in addition to color)
--* Screen reader compatible (ARIA labels for claim credibility)
--* Keyboard navigation to highlighted claims
--
--**Implementation Notes**:
--* Claims extracted and analyzed by AKEL during initial processing
--* Highlighting data stored as annotations with byte offsets
--* Client-side rendering of highlights based on verdict data
--* Mobile responsive (tap instead of hover)
--
  === 8.5 Workflow & Moderation ===
  ==== FR9 — Publication Workflow ====
@@ -728,500 +728,78 @@
  * Continuous integration
  * Comprehensive documentation
--=== NFR11: AKEL Quality Assurance Framework ===
++== 10. MVP Scope ==
--**Fulfills:** AI safety, IFCN methodology transparency
++**Phase 1 (Months 1-3): Read-Only MVP**
--**Specification:**
++Build:
++* Automated claim analysis
++* Confidence scoring
++* Source evaluation
++* Browse/search interface
++* User flagging system
--Multi-layer AI quality gates to detect hallucinations, low-confidence results, and logical inconsistencies.
++**Goal**: Prove AI quality before adding user editing
--==== Quality Gate 1: Claim Extraction Validation ====
++**User Needs fulfilled in Phase 1**: UN-1, UN-2, UN-3, UN-4, UN-5, UN-6, UN-7, UN-8, UN-9, UN-12
--**Purpose:** Ensure extracted claims are factual assertions (not opinions/predictions)
++**Phase 2 (Months 4-6): User Contributions**
--**Checks:**
--1. **Factual Statement Test:** Is this verifiable? (Yes/No)
--2. **Opinion Detection:** Contains hedging language? ("I think", "probably", "best")
--3. **Future Prediction Test:** Makes claims about future events?
--4. **Specificity Score:** Contains specific entities, numbers, dates?
++Add only if needed:
++* Simple editing (Wikipedia-style)
++* Reputation system
++* Basic moderation
--**Thresholds:**
--* Factual: Must be "Yes"
--* Opinion markers: <2 hedging phrases
--* Specificity: ≥3 specific elements
++**Additional User Needs fulfilled**: UN-13
--**Action if Failed:** Flag as "Non-verifiable", do NOT generate verdict
++**Phase 3 (Months 7-12): Refinement**
--==== Quality Gate 2: Evidence Relevance Validation ====
++* Continuous quality improvement
++* Feature additions based on real usage
++* Scale infrastructure
--**Purpose:** Ensure AI-linked evidence actually relates to claim
++**Additional User Needs fulfilled**: UN-14 (API access), UN-15 (Full evolution tracking)
--**Checks:**
--1. **Semantic Similarity Score:** Evidence vs. claim (embeddings)
--2. **Entity Overlap:** Shared people/places/things?
--3. **Topic Relevance:** Discusses claim subject?
++**Deferred**:
++* Federation (until multiple successful instances exist)
++* Complex contribution workflows (focus on automation)
++* Extensive role hierarchy (keep simple)
--**Thresholds:**
--* Similarity: ≥0.6 (cosine similarity)
--* Entity overlap: ≥1 shared entity
--* Topic relevance: ≥0.5
++== 11. Success Metrics ==
--**Action if Failed:** Discard irrelevant evidence
++**System Quality** (track weekly):
++* Error rate by category (target: -10%/month)
++* Average confidence score (target: increase)
++* Source quality distribution (target: more high-quality)
++* Contradiction detection rate (target: increase)
--==== Quality Gate 3: Scenario Coherence Check ====
++**Efficiency** (track monthly):
++* Claims processed per hour (target: increase)
++* Human hours per claim (target: decrease)
++* Automation coverage (target: >90%)
++* Re-work rate (target: <5%)
--**Purpose:** Validate scenario assumptions are logical and complete
++**User Satisfaction** (track quarterly):
++* User flag rate (issues found)
++* Correction acceptance rate (flags valid)
++* Return user rate
++* Trust indicators (surveys)
--**Checks:**
--1. **Completeness:** All required fields populated
--2. **Internal Consistency:** Assumptions don't contradict
--3. **Distinguishability:** Scenarios meaningfully different
++**User Needs Metrics** (track quarterly):
++* UN-1: % users who understand trust scores
++* UN-4: Time to verify social media claim (target: <30s)
++* UN-7: % users who access evidence details
++* UN-8: % users who view multiple scenarios
++* UN-15: % users who check evolution timeline
--**Thresholds:**
--* Required fields: 100%
--* Contradiction score: <0.3
--* Scenario similarity: <0.8
++== 12. Requirements Traceability ==
--**Action if Failed:** Merge duplicates, reduce confidence -20%
--
--==== Quality Gate 4: Verdict Confidence Assessment ====
--
--**Purpose:** Only publish high-confidence verdicts
--
--**Checks:**
--1. **Evidence Count:** Minimum 2 sources
--2. **Source Quality:** Average reliability ≥0.6
--3. **Evidence Agreement:** Supporting vs. contradicting ≥0.6
--4. **Uncertainty Factors:** Hedging in reasoning
--
--**Confidence Tiers:**
--* **HIGH (80-100%):** ≥3 sources, ≥0.7 quality, ≥80% agreement
--* **MEDIUM (50-79%):** ≥2 sources, ≥0.6 quality, ≥60% agreement
--* **LOW (0-49%):** <2 sources OR low quality/agreement
--* **INSUFFICIENT:** <2 sources → DO NOT PUBLISH
--
--**Implementation Phases:**
--* **POC1:** Gates 1 & 4 only (basic validation)
--* **POC2:** All 4 gates (complete framework)
--* **V1.0:** Hardened with <5% hallucination rate
--
--**Acceptance Criteria:**
--* ✅ All gates operational
--* ✅ Hallucination rate <5%
--* ✅ Quality metrics public
--
--=== NFR12: Security Controls ===
--
--**Fulfills:** Data protection, system integrity, user privacy, production readiness
--
--**Purpose:** Protect FactHarbor systems, user data, and operations from security threats, ensuring production-grade security posture.
--
--**Specification:**
--
--==== API Security ====
--
--**Rate Limiting:**
--* **Analysis endpoints:** 100 requests/hour per IP
--* **Read endpoints:** 1,000 requests/hour per IP
--* **Search:** 500 requests/hour per IP
--* **Authenticated users:** 5x higher limits
--* **Burst protection:** Max 10 requests/second
--
--**Authentication & Authorization:**
--* **API Keys:** Required for programmatic access
--* **JWT tokens:** For user sessions (1-hour expiry)
--* **OAuth2:** For third-party integrations
--* **Role-Based Access Control (RBAC):**
-- * Public: Read-only access to published claims
-- * Contributor: Submit claims, provide evidence
-- * Moderator: Review contributions, manage quality
-- * Admin: System configuration, user management
--
--**CORS Policies:**
--* Whitelist approved domains only
--* No wildcard origins in production
--* Credentials required for sensitive endpoints
--
--**Input Sanitization:**
--* Validate all user input against schemas
--* Sanitize HTML/JavaScript in text submissions
--* Prevent SQL injection (use parameterized queries)
--* Prevent command injection (no shell execution of user input)
--* Max request size: 10MB
--* File upload restrictions: Whitelist file types, scan for malware
--
-----
--
--==== Data Security ====
--
--**Encryption at Rest:**
--* Database encryption using AES-256
--* Encrypted backups
--* Key management via cloud provider KMS (AWS KMS, Google Cloud KMS)
--* Regular key rotation (90-day cycle)
--
--**Encryption in Transit:**
--* HTTPS/TLS 1.3 only (no TLS 1.0/1.1)
--* Strong cipher suites only
--* HSTS (HTTP Strict Transport Security) enabled
--* Certificate pinning for mobile apps
--
--**Secure Credential Storage:**
--* Passwords hashed with bcrypt (cost factor 12+)
--* API keys encrypted in database
--* Secrets stored in environment variables (never in code)
--* Use secrets manager (AWS Secrets Manager, HashiCorp Vault)
--
--**Data Privacy:**
--* Minimal data collection (privacy by design)
--* User data deletion on request (GDPR compliance)
--* PII encryption in database
--* Anonymize logs (no PII in log files)
--
-----
--
--==== Application Security ====
--
--**OWASP Top 10 Compliance:**
--
--1. **Broken Access Control:** RBAC implementation, path traversal prevention
--2. **Cryptographic Failures:** Strong encryption, secure key management
--3. **Injection:** Parameterized queries, input validation
--4. **Insecure Design:** Security review of all features
--5. **Security Misconfiguration:** Hardened defaults, security headers
--6. **Vulnerable Components:** Dependency scanning (see below)
--7. **Authentication Failures:** Strong password policy, MFA support
--8. **Data Integrity Failures:** Signature verification, checksums
--9. **Security Logging Failures:** Comprehensive audit logs
--10. **Server-Side Request Forgery:** URL validation, whitelist domains
--
--**Security Headers:**
--* `Content-Security-Policy`: Strict CSP to prevent XSS
--* `X-Frame-Options`: DENY (prevent clickjacking)
--* `X-Content-Type-Options`: nosniff
--* `Referrer-Policy`: strict-origin-when-cross-origin
--* `Permissions-Policy`: Restrict browser features
--
--**Dependency Vulnerability Scanning:**
--* **Tools:** Snyk, Dependabot, npm audit, pip-audit
--* **Frequency:** Daily automated scans
--* **Action:** Patch critical vulnerabilities within 24 hours
--* **Policy:** No known high/critical CVEs in production
--
--**Security Audits:**
--* **Internal:** Quarterly security reviews
--* **External:** Annual penetration testing by certified firm
--* **Bug Bounty:** Public bug bounty program (V1.1+)
--* **Compliance:** SOC 2 Type II certification target (V1.5)
--
-----
--
--==== Operational Security ====
--
--**DDoS Protection:**
--* CloudFlare or AWS Shield
--* Rate limiting at CDN layer
--* Automatic IP blocking for abuse patterns
--
--**Monitoring & Alerting:**
--* Real-time security event monitoring
--* Alerts for:
-- * Failed login attempts (>5 in 10 minutes)
-- * API abuse patterns
-- * Unusual data access patterns
-- * Security scan detections
--* Integration with SIEM (Security Information and Event Management)
--
--**Incident Response:**
--* Documented incident response plan
--* Security incident classification (P1-P4)
--* On-call rotation for security issues
--* Post-mortem for all security incidents
--* Public disclosure policy (coordinated disclosure)
--
--**Backup & Recovery:**
--* Daily encrypted backups
--* 30-day retention period
--* Tested recovery procedures (quarterly)
--* Disaster recovery plan (RTO: 4 hours, RPO: 1 hour)
--
-----
--
--==== Compliance & Standards ====
--
--**GDPR Compliance:**
--* User consent management
--* Right to access data
--* Right to deletion
--* Data portability
--* Privacy policy published
--
--**Accessibility:**
--* WCAG 2.1 AA compliance
--* Screen reader compatibility
--* Keyboard navigation
--* Alt text for images
--
--**Browser Support:**
--* Modern browsers only (Chrome/Edge/Firefox/Safari latest 2 versions)
--* No IE11 support
--
--**Acceptance Criteria:**
--
--* ✅ Passes OWASP ZAP security scan (no high/critical findings)
--* ✅ All dependencies with known vulnerabilities patched
--* ✅ Penetration test completed with no critical findings
--* ✅ Rate limiting blocks abuse attempts
--* ✅ Encryption at rest and in transit verified
--* ✅ Security headers scored A+ on securityheaders.com
--* ✅ Incident response plan documented and tested
--* ✅ 95% uptime over 30-day period
--
--=== NFR13: Quality Metrics Transparency ===
--
--**Fulfills:** User trust, transparency, continuous improvement, IFCN methodology transparency
--
--**Purpose:** Provide transparent, measurable quality metrics that demonstrate AKEL's performance and build user trust in automated fact-checking.
--
--**Specification:**
--
--==== Component: Public Quality Dashboard ====
--
--**Core Metrics to Display:**
--
--**1. Verdict Quality Metrics**
--
--**TIGERScore (Fact-Checking Quality):**
--* **Definition:** Measures how well generated verdicts match expert fact-checker judgments
--* **Scale:** 0-100 (higher is better)
--* **Calculation:** Using TIGERScore framework (Truth-conditional accuracy, Informativeness, Generality, Evaluativeness, Relevance)
--* **Target:** Average ≥80 for production release
--* **Display:**
--{{code}}
--Verdict Quality (TIGERScore):
--Overall: 84.2 ▲ (+2.1 from last month)
--
--Distribution:
-- Excellent (>80): 67%
-- Good (60-80): 28%
-- Needs Improvement (<60): 5%
--
--Trend: [Graph showing improvement over time]
--{{/code}}
--
--**2. Hallucination & Faithfulness Metrics**
--
--**AlignScore (Faithfulness to Evidence):**
--* **Definition:** Measures how well verdicts align with actual evidence content
--* **Scale:** 0-1 (higher is better)
--* **Purpose:** Detect AI hallucinations (making claims not supported by evidence)
--* **Target:** Average ≥0.85, hallucination rate <5%
--* **Display:**
--{{code}}
--Evidence Faithfulness (AlignScore):
--Average: 0.87 ▼ (-0.02 from last month)
--
--Hallucination Rate: 4.2%
-- - Claims without evidence support: 3.1%
-- - Misrepresented evidence: 1.1%
--
--Action: Prompt engineering review scheduled
--{{/code}}
--
--**3. Evidence Quality Metrics**
--
--**Source Reliability:**
--* Average source quality score (0-1 scale)
--* Distribution of high/medium/low quality sources
--* Publisher track record trends
--
--**Evidence Coverage:**
--* Average number of sources per claim
--* Percentage of claims with ≥2 sources (EFCSN minimum)
--* Geographic diversity of sources
--
--**Display:**
--{{code}}
--Evidence Quality:
--
--Average Sources per Claim: 4.2
--Claims with ≥2 sources: 94% (EFCSN compliant)
--
--Source Quality Distribution:
-- High quality (>0.8): 48%
-- Medium quality (0.5-0.8): 43%
-- Low quality (<0.5): 9%
--
--Geographic Diversity: 23 countries represented
--{{/code}}
--
--**4. Contributor Consensus Metrics** (when human reviewers involved)
--
--**Inter-Rater Reliability (IRR):**
--* **Calculation:** Cohen's Kappa or Fleiss' Kappa for multiple raters
--* **Scale:** 0-1 (higher is better)
--* **Interpretation:**
-- * >0.8: Almost perfect agreement
-- * 0.6-0.8: Substantial agreement
-- * 0.4-0.6: Moderate agreement
-- * <0.4: Poor agreement
--* **Target:** Maintain ≥0.7 (substantial agreement)
--
--**Display:**
--{{code}}
--Contributor Consensus:
--
--Inter-Rater Reliability (IRR): 0.73 (Substantial agreement)
-- - Verdict agreement: 78%
-- - Evidence quality agreement: 71%
-- - Scenario structure agreement: 69%
--
--Cases requiring moderator review: 12
--Moderator override rate: 8%
--{{/code}}
--
-----
--
--==== Quality Dashboard Implementation ====
--
--**Dashboard Location:** `/quality-metrics`
--
--**Update Frequency:**
--* **POC2:** Weekly manual updates
--* **Beta 0:** Daily automated updates
--* **V1.0:** Real-time metrics (updated hourly)
--
--**Dashboard Sections:**
--
--1. **Overview:** Key metrics at a glance
--2. **Verdict Quality:** TIGERScore trends and distributions
--3. **Evidence Analysis:** Source quality and coverage
--4. **AI Performance:** Hallucination rates, AlignScore
--5. **Human Oversight:** Contributor consensus, review rates
--6. **System Health:** Processing times, error rates, uptime
--
--**Example Dashboard Layout:**
--
--{{code}}
--┌─────────────────────────────────────────────────────────────┐
--│ FactHarbor Quality Metrics Last updated: │
--│ Public Dashboard 2 hours ago │
--└─────────────────────────────────────────────────────────────┘
--
--📊 KEY METRICS
--─────────────────────────────────────────────────────────────
--TIGERScore (Verdict Quality): 84.2 ▲ (+2.1)
--AlignScore (Faithfulness): 0.87 ▼ (-0.02)
--Hallucination Rate: 4.2% ✓ (Target: <5%)
--Average Sources per Claim: 4.2 ▲ (+0.3)
--
--📈 TRENDS (30 days)
--─────────────────────────────────────────────────────────────
--[Graph: TIGERScore trending upward]
--[Graph: Hallucination rate declining]
--[Graph: Evidence quality stable]
--
--⚠️ IMPROVEMENT TARGETS
--─────────────────────────────────────────────────────────────
--1. Reduce hallucination rate to <3% (Current: 4.2%)
--2. Increase TIGERScore average to >85 (Current: 84.2)
--3. Maintain IRR >0.75 (Current: 0.73)
--
--📄 DETAILED REPORTS
--─────────────────────────────────────────────────────────────
--• Monthly Quality Report (PDF)
--• Methodology Documentation
--• AKEL Performance Analysis
--• Contributor Agreement Analysis
--
--{{/code}}
--
-----
--
--==== Continuous Improvement Feedback Loop ====
--
--**How Metrics Inform AKEL Improvements:**
--
--1. **Identify Weak Areas:**
-- * Low TIGERScore → Review prompt engineering
-- * High hallucination → Strengthen evidence grounding
-- * Low IRR → Clarify evaluation criteria
--
--2. **A/B Testing Integration:**
-- * Test prompt variations
-- * Measure impact on quality metrics
-- * Deploy winners automatically
--
--3. **Alert Thresholds:**
-- * TIGERScore drops below 75 → Alert team
-- * Hallucination rate exceeds 7% → Pause auto-publishing
-- * IRR below 0.6 → Moderator training needed
--
--4. **Monthly Quality Reviews:**
-- * Analyze trends
-- * Identify systematic issues
-- * Plan prompt improvements
-- * Update AKEL models
--
-----
--
--==== Metric Calculation Details ====
--
--**TIGERScore Implementation:**
--* Reference: https://github.com/TIGER-AI-Lab/TIGERScore
--* Input: Generated verdict + reference verdict (from expert)
--* Output: 0-100 score across 5 dimensions
--* Requires: Test set of expert-reviewed claims (minimum 100)
--
--**AlignScore Implementation:**
--* Reference: https://github.com/yuh-zha/AlignScore
--* Input: Generated verdict + source evidence text
--* Output: 0-1 faithfulness score
--* Calculation: Semantic alignment between claim and evidence
--
--**Source Quality Scoring:**
--* Use existing source reliability database (e.g., NewsGuard, MBFC)
--* Factor in: Publication history, corrections record, transparency
--* Scale: 0-1 (weighted average across sources)
--
-----
--
--==== Integration Points ====
--
--* **NFR11: AKEL Quality Assurance** - Metrics validate quality gate effectiveness
--* **FR49: A/B Testing** - Metrics measure test success
--* **FR11: Audit Trail** - Source of quality data
--* **NFR3: Transparency** - Public metrics build trust
--
--**Acceptance Criteria:**
--
--* ✅ All core metrics implemented and calculating correctly
--* ✅ Dashboard updates daily (Beta 0) or hourly (V1.0)
--* ✅ Alerts trigger when metrics degrade beyond thresholds
--* ✅ Monthly quality report auto-generates
--* ✅ Dashboard is publicly accessible (no login required)
--* ✅ Mobile-responsive dashboard design
--* ✅ Metrics inform quarterly AKEL improvement planning
--
--== 13. Requirements Traceability ==
--
  For full traceability matrix showing which requirements fulfill which user needs, see:
  * [[User Needs>>FactHarbor.Specification.Requirements.User Needs.WebHome]] - Section 8 includes comprehensive mapping tables
--== 14. Related Pages ==
++== 13. Related Pages ==
--**Non-Functional Requirements (see Section 9):**
--* [[NFR11 — AKEL Quality Assurance Framework>>#NFR11]]
--* [[NFR12 — Security Controls>>#NFR12]]
--* [[NFR13 — Quality Metrics Transparency>>#NFR13]]
--
--**Other Requirements:**
--* [[User Needs>>FactHarbor.Specification.Requirements.User Needs.WebHome]]
--* [[V1.0 Requirements>>FactHarbor.Specification.Requirements.V10.]]
--* [[Gap Analysis>>FactHarbor.Specification.Requirements.GapAnalysis]]
--
  * **[[User Needs>>FactHarbor.Specification.Requirements.User Needs.WebHome]]** - What users need (drives these requirements)
  * [[Architecture>>FactHarbor.Specification.Architecture.WebHome]] - How requirements are implemented
  * [[Data Model>>FactHarbor.Specification.Data Model.WebHome]] - Data structures supporting requirements
@@ -1229,745 +1229,3 @@
  * [[AKEL>>FactHarbor.Specification.AI Knowledge Extraction Layer (AKEL).WebHome]] - AI system fulfilling automation requirements
  * [[Global Rules>>FactHarbor.Organisation.How-We-Work-Together.GlobalRules.WebHome]]
  * [[Privacy Policy>>FactHarbor.Organisation.How-We-Work-Together.Privacy-Policy]]
--
--= V0.9.70 Additional Requirements =
--
--== Functional Requirements (Additional) ==
--
--=== FR44: ClaimReview Schema Implementation ===
--
--**Fulfills:** UN-13 (Cite FactHarbor Verdicts), UN-14 (API Access for Integration), UN-26 (Search Engine Visibility)
--
--**Purpose:** Generate valid ClaimReview structured data for every published analysis to enable Google/Bing search visibility and fact-check discovery.
--
--**Specification:**
--
--==== Component: Schema.org Markup Generator ====
--
--FactHarbor must generate valid ClaimReview structured data following Schema.org specifications for every published claim analysis.
--
--**Required JSON-LD Schema:**
--
--{{code language="json"}}
--{
-- "@context": "https://schema.org",
-- "@type": "ClaimReview",
-- "datePublished": "YYYY-MM-DD",
-- "url": "https://factharbor.org/claims/{claim_id}",
-- "claimReviewed": "The exact claim text",
-- "author": {
-- "@type": "Organization",
-- "name": "FactHarbor",
-- "url": "https://factharbor.org"
-- },
-- "reviewRating": {
-- "@type": "Rating",
-- "ratingValue": "1-5",
-- "bestRating": "5",
-- "worstRating": "1",
-- "alternateName": "FactHarbor likelihood score"
-- },
-- "itemReviewed": {
-- "@type": "Claim",
-- "author": {
-- "@type": "Person",
-- "name": "Claim author if known"
-- },
-- "datePublished": "YYYY-MM-DD if known",
-- "appearance": {
-- "@type": "CreativeWork",
-- "url": "Original claim URL if from article"
-- }
-- }
--}
--{{/code}}
--
--**FactHarbor-Specific Mapping:**
--
--**Likelihood Score to Rating Scale:**
--* 80-100% likelihood → 5 (Highly Supported)
--* 60-79% likelihood → 4 (Supported)
--* 40-59% likelihood → 3 (Mixed/Uncertain)
--* 20-39% likelihood → 2 (Questionable)
--* 0-19% likelihood → 1 (Refuted)
--
--**Multiple Scenarios Handling:**
--* If claim has multiple scenarios with different verdicts, generate **separate ClaimReview** for each scenario
--* Add `disambiguatingDescription` field explaining scenario context
--* Example: "Scenario: If interpreted as referring to 2023 data..."
--
--==== Implementation Requirements ====
--
--1. **Auto-generate** on claim publication
--2. **Embed** in HTML `<head>` section as JSON-LD script
--3. **Validate** against Schema.org validator before publishing
--4. **Submit** to Google Search Console for indexing
--5. **Update** automatically when verdict changes (integrate with FR8: Time Evolution)
--
--==== Integration Points ====
--
--* **FR7: Automated Verdicts** - Source of rating data and claim text
--* **FR8: Time Evolution** - Triggers schema updates when verdicts change
--* **FR11: Audit Trail** - Logs all schema generation and update events
--
--==== Resources ====
--
--* ClaimReview Project: https://www.claimreviewproject.com
--* Schema.org ClaimReview: https://schema.org/ClaimReview
--* Google Fact Check Guidelines: https://developers.google.com/search/docs/appearance/fact-check
--
--**Acceptance Criteria:**
--
--* ✅ Passes Google Structured Data Testing Tool
--* ✅ Appears in Google Fact Check Explorer within 48 hours of publication
--* ✅ Valid JSON-LD syntax (no errors)
--* ✅ All required fields populated with correct data types
--* ✅ Handles multi-scenario claims correctly (separate ClaimReview per scenario)
--
--=== FR45: User Corrections Notification System ===
--
--**Fulfills:** IFCN Principle 5 (Open & Honest Corrections), EFCSN compliance
--
--**Purpose:** When any claim analysis is corrected, notify users who previously viewed the claim to maintain transparency and build trust.
--
--**Specification:**
--
--==== Component: Corrections Visibility Framework ====
--
--**Correction Types:**
--
--1. **Major Correction:** Verdict changes category (e.g., "Supported" → "Refuted")
--2. **Significant Correction:** Likelihood score changes >20%
--3. **Minor Correction:** Evidence additions, source quality updates
--4. **Scenario Addition:** New scenario added to existing claim
--
--==== Notification Mechanisms ====
--
--**1. In-Page Banner:**
--
--Display prominent banner on claim page:
--
--{{code}}
--[!] CORRECTION NOTICE
--This analysis was updated on [DATE]. [View what changed] [Dismiss]
--
--Major changes:
--• Verdict changed from "Likely True (75%)" to "Uncertain (45%)"
--• New contradicting evidence added from [Source]
--• Scenario 2 updated with additional context
--
--[See full correction log]
--{{/code}}
--
--**2. Correction Log Page:**
--
--* Public changelog at `/claims/{id}/corrections`
--* Displays for each correction:
-- * Date/time of correction
-- * What changed (before/after comparison)
-- * Why changed (reason if provided)
-- * Who made change (AKEL auto-update vs. contributor override)
--
--**3. Email Notifications (opt-in):**
--
--* Send to users who bookmarked or shared the claim
--* Subject: "FactHarbor Correction: [Claim title]"
--* Include summary of changes
--* Link to updated analysis
--
--**4. RSS/API Feed:**
--
--* Corrections feed at `/corrections.rss`
--* API endpoint: `GET /api/corrections?since={timestamp}`
--* Enables external monitoring by journalists and researchers
--
--==== Display Rules ====
--
--* Show banner on **ALL pages** displaying the claim (search results, related claims, embeddings)
--* Banner persists for **30 days** after correction
--* **"Corrections" count badge** on claim card
--* **Timestamp** on every verdict: "Last updated: [datetime]"
--
--==== IFCN Compliance Requirements ====
--
--* Corrections policy published at `/corrections-policy`
--* User can report suspected errors via `/report-error/{claim_id}`
--* Link to IFCN complaint process (if FactHarbor becomes signatory)
--* **Scrupulous transparency:** Never silently edit analyses
--
--==== Integration Points ====
--
--* **FR8: Time Evolution** - Triggers corrections when verdicts change
--* **FR11: Audit Trail** - Source of correction data and change history
--* **NFR3: Transparency** - Public correction log demonstrates commitment
--
--**Acceptance Criteria:**
--
--* ✅ Banner appears within 60 seconds of correction
--* ✅ Correction log is permanent and publicly accessible
--* ✅ Email notifications deliver within 5 minutes
--* ✅ RSS feed updates in real-time
--* ✅ Mobile-responsive banner design
--* ✅ Accessible (screen reader compatible)
--
--=== FR46: Image Verification System ===
--
--**Fulfills:** UN-27 (Visual Claim Verification)
--
--**Purpose:** Verify authenticity and context of images shared with claims to detect manipulation, misattribution, and out-of-context usage.
--
--**Specification:**
--
--==== Component: Multi-Method Image Verification ====
--
--**Method 1: Reverse Image Search**
--
--**Purpose:** Find earlier uses of the image to verify context
--
--**Implementation:**
--* Integrate APIs:
-- * **Google Vision AI** (reverse search)
-- * **TinEye** (oldest known uses)
-- * **Bing Visual Search** (broad coverage)
--
--**Process:**
--1. Extract image from claim or user upload
--2. Query multiple reverse search services
--3. Analyze results for:
-- * Earliest known publication
-- * Original context (what was it really showing?)
-- * Publication timeline
-- * Geographic spread
--
--**Output:**
--{{code}}
--Reverse Image Search Results:
--
--Earliest known use: 2019-03-15 (5 years before claim)
--Original context: "Photo from 2019 flooding in Mumbai"
--This claim uses it for: "2024 hurricane damage in Florida"
--
--⚠️ Image is OUT OF CONTEXT
--
--Found in 47 other articles:
--• 2019-03-15: Mumbai floods (original)
--• 2020-07-22: Bangladesh monsoon
--• 2024-10-15: Current claim (misattributed)
--
--[View full timeline]
--{{/code}}
--
-----
--
--**Method 2: AI Manipulation Detection**
--
--**Purpose:** Detect deepfakes, face swaps, and digital alterations
--
--**Implementation:**
--* Integrate detection services:
-- * **Sensity AI** (deepfake detection)
-- * **Reality Defender** (multimodal analysis)
-- * **AWS Rekognition** (face detection inconsistencies)
--
--**Detection Categories:**
--1. **Face Manipulation:**
-- * Deepfake face swaps
-- * Expression manipulation
-- * Identity replacement
--
--2. **Image Manipulation:**
-- * Copy-paste artifacts
-- * Clone stamp detection
-- * Content-aware fill detection
-- * JPEG compression inconsistencies
--
--3. **AI Generation:**
-- * Detect fully AI-generated images
-- * Identify generation artifacts
-- * Check for model signatures
--
--**Confidence Scoring:**
--* **HIGH (80-100%):** Strong evidence of manipulation
--* **MEDIUM (50-79%):** Suspicious artifacts detected
--* **LOW (0-49%):** Minor inconsistencies or inconclusive
--
--**Output:**
--{{code}}
--Manipulation Analysis:
--
--Face Manipulation: LOW RISK (12%)
--Image Editing: MEDIUM RISK (64%)
-- • Clone stamp artifacts detected in sky region
-- • JPEG compression inconsistent between objects
--
--AI Generation: LOW RISK (8%)
--
--⚠️ Possible manipulation detected. Manual review recommended.
--{{/code}}
--
-----
--
--**Method 3: Metadata Analysis (EXIF)**
--
--**Purpose:** Extract technical details that may reveal manipulation or misattribution
--
--**Extracted Data:**
--* **Camera/Device:** Make, model, software
--* **Timestamps:** Original date, modification dates
--* **Location:** GPS coordinates (if present)
--* **Editing History:** Software used, edit count
--* **File Properties:** Resolution, compression, format conversions
--
--**Red Flags:**
--* Metadata completely stripped (suspicious)
--* Timestamp conflicts with claimed date
--* GPS location conflicts with claimed location
--* Multiple edit rounds (hiding something?)
--* Creation date after modification date (impossible)
--
--**Output:**
--{{code}}
--Image Metadata:
--
--Camera: iPhone 14 Pro
--Original date: 2023-08-12 14:32:15
--Location: 40.7128°N, 74.0060°W (New York City)
--Modified: 2024-10-15 08:45:22
--Software: Adobe Photoshop 2024
--
--⚠️ Location conflicts with claim
--Claim says: "Taken in Los Angeles"
--EXIF says: New York City
--
--⚠️ Edited 14 months after capture
--{{/code}}
--
-----
--
--==== Verification Workflow ====
--
--**Automatic Triggers:**
--1. User submits claim with image
--2. Article being analyzed contains images
--3. Social media post includes photos
--
--**Process:**
--1. Extract images from content
--2. Run all 3 verification methods in parallel
--3. Aggregate results into confidence score
--4. Generate human-readable summary
--5. Display prominently in analysis
--
--**Display Integration:**
--
--Show image verification panel in claim analysis:
--
--{{code}}
--📷 IMAGE VERIFICATION
--
--[Image thumbnail]
--
--✅ Reverse Search: Original context verified
--⚠️ Manipulation: Possible editing detected (64% confidence)
--✅ Metadata: Consistent with claim details
--
--Overall Assessment: CAUTION ADVISED
--This image may have been edited. Original context appears accurate.
--
--[View detailed analysis]
--{{/code}}
--
--==== Integration Points ====
--
--* **FR7: Automated Verdicts** - Image verification affects claim credibility
--* **FR4: Analysis Summary** - Image findings included in summary
--* **UN-27: Visual Claim Verification** - Direct fulfillment
--
--==== Cost Considerations ====
--
--**API Costs (estimated per image):**
--* Google Vision AI: $0.001-0.003
--* TinEye: $0.02 (commercial API)
--* Sensity AI: $0.05-0.10
--* AWS Rekognition: $0.001-0.002
--
--**Total per image:** ~$0.07-0.15
--
--**Mitigation Strategies:**
--* Cache results for duplicate images
--* Use free tier quotas where available
--* Prioritize higher-value claims for deep analysis
--* Offer premium verification as paid tier
--
--**Acceptance Criteria:**
--
--* ✅ Reverse image search finds original sources
--* ✅ Manipulation detection accuracy >80% on test dataset
--* ✅ EXIF extraction works for major image formats (JPEG, PNG, HEIC)
--* ✅ Results display within 10 seconds
--* ✅ Mobile-friendly image comparison interface
--* ✅ False positive rate <15%
--
--=== FR47: Archive.org Integration ===
--
--**Importance:** CRITICAL
--**Fulfills:** Evidence persistence, FR5 (Evidence linking)
--
--**Purpose:** Ensure evidence remains accessible even if original sources are deleted.
--
--**Specification:**
--
--**Automatic Archiving:**
--
--When AKEL links evidence:
--1. Check if URL already archived (Wayback Machine API)
--2. If not, submit for archiving (Save Page Now API)
--3. Store both original URL and archive URL
--4. Display both to users
--
--**Archive Display:**
--
--{{code}}
--Evidence Source: [Original URL]
--Archived: [Archive.org URL] (Captured: [date])
--
--[View Original] [View Archive]
--{{/code}}
--
--**Fallback Logic:**
--
--* If original URL unavailable → Auto-redirect to archive
--* If archive unavailable → Display warning
--* If both unavailable → Flag for manual review
--
--**API Integration:**
--
--* Use Wayback Machine Availability API
--* Use Save Page Now API (SPNv2)
--* Rate limiting: 15 requests/minute (Wayback limit)
--
--**Acceptance Criteria:**
--
--* ✅ All evidence URLs auto-archived
--* ✅ Archive links displayed to users
--* ✅ Fallback to archive if original unavailable
--* ✅ API rate limits respected
--* ✅ Archive status visible in evidence display
--
--== Category 4: Community Safety ===== FR48: Contributor Safety Framework ===
--
--**Importance:** CRITICAL
--**Fulfills:** UN-28 (Safe contribution environment)
--
--**Purpose:** Protect contributors from harassment, doxxing, and coordinated attacks.
--
--**Specification:**
--
--**1. Privacy Protection:**
--
--* **Optional Pseudonymity:** Contributors can use pseudonyms
--* **Email Privacy:** Emails never displayed publicly
--* **Profile Privacy:** Contributors control what's public
--* **IP Logging:** Only for abuse prevention, not public
--
--**2. Harassment Prevention:**
--
--* **Automated Toxicity Detection:** Flag abusive comments
--* **Personal Information Detection:** Auto-block doxxing attempts
--* **Coordinated Attack Detection:** Identify brigading patterns
--* **Rapid Response:** Moderator alerts for harassment
--
--**3. Safety Features:**
--
--* **Block Users:** Contributors can block harassers
--* **Private Contributions:** Option to contribute anonymously
--* **Report Harassment:** One-click harassment reporting
--* **Safety Resources:** Links to support resources
--
--**4. Moderator Tools:**
--
--* **Quick Ban:** Immediately block abusers
--* **Pattern Detection:** Identify coordinated attacks
--* **Appeal Process:** Fair review of moderation actions
--* **Escalation:** Serious threats escalated to authorities
--
--**5. Trusted Contributor Protection:**
--
--* **Enhanced Privacy:** Additional protection for high-profile contributors
--* **Verification:** Optional identity verification (not public)
--* **Legal Support:** Resources for contributors facing legal threats
--
--**Acceptance Criteria:**
--
--* ✅ Pseudonyms supported
--* ✅ Toxicity detection active
--* ✅ Doxxing auto-blocked
--* ✅ Harassment reporting functional
--* ✅ Moderator tools implemented
--* ✅ Safety policy published
--
--== Category 5: Continuous Improvement ===== FR49: A/B Testing Framework ===
--
--**Importance:** CRITICAL
--**Fulfills:** Continuous system improvement
--
--**Purpose:** Test and measure improvements to AKEL prompts, algorithms, and workflows.
--
--**Specification:**
--
--**Test Capabilities:**
--
--1. **Prompt Variations:**
-- * Test different claim extraction prompts
-- * Test different verdict generation prompts
-- * Measure: Accuracy, clarity, completeness
--
--2. **Algorithm Variations:**
-- * Test different source scoring algorithms
-- * Test different confidence calculations
-- * Measure: Audit accuracy, user satisfaction
--
--3. **Workflow Variations:**
-- * Test different quality gate thresholds
-- * Test different risk tier assignments
-- * Measure: Publication rate, quality scores
--
--**Implementation:**
--
--* **Traffic Split:** 50/50 or 90/10 splits
--* **Randomization:** Consistent per claim (not per user)
--* **Metrics Collection:** Automatic for all variants
--* **Statistical Significance:** Minimum sample size calculation
--* **Rollout:** Winner promoted to 100% traffic
--
--**A/B Test Workflow:**
--
--{{code}}
--1. Hypothesis: "New prompt improves claim extraction"
--2. Design test: Control vs. Variant
--3. Define metrics: Extraction accuracy, completeness
--4. Run test: 7-14 days, minimum 100 claims each
--5. Analyze results: Statistical significance?
--6. Decision: Deploy winner or iterate
--{{/code}}
--
--**Acceptance Criteria:**
--
--* ✅ A/B testing framework implemented
--* ✅ Can test prompt variations
--* ✅ Can test algorithm variations
--* ✅ Metrics automatically collected
--* ✅ Statistical significance calculated
--* ✅ Results inform system improvements
--
--=== FR54: Evidence Deduplication ===
--
--**Importance:** CRITICAL (POC2/Beta)
--**Fulfills:** Accurate evidence counting, quality metrics
--
--**Purpose:** Avoid counting the same source multiple times when it appears in different forms.
--
--**Specification:**
--
--**Deduplication Logic:**
--
--1. **URL Normalization:**
-- * Remove tracking parameters (?utm_source=...)
-- * Normalize http/https
-- * Normalize www/non-www
-- * Handle redirects
--
--2. **Content Similarity:**
-- * If two sources have >90% text similarity → Same source
-- * If one is subset of other → Same source
-- * Use fuzzy matching for minor differences
--
--3. **Cross-Domain Syndication:**
-- * Detect wire service content (AP, Reuters)
-- * Mark as single source if syndicated
-- * Count original publication only
--
--**Display:**
--
--{{code}}
--Evidence Sources (3 unique, 5 total):
--
--1. Original Article (NYTimes)
-- - Also appeared in: WashPost, Guardian (syndicated)
--
--2. Research Paper (Nature)
--
--3. Official Statement (WHO)
--{{/code}}
--
--**Acceptance Criteria:**
--
--* ✅ URL normalization works
--* ✅ Content similarity detected
--* ✅ Syndicated content identified
--* ✅ Unique vs. total counts accurate
--* ✅ Improves evidence quality metrics
--
--== Additional Requirements (Lower Importance) ===== FR50: OSINT Toolkit Integration ===
--
--**Fulfills:** Advanced media verification
--
--**Purpose:** Integrate open-source intelligence tools for advanced verification.
--
--**Tools to Integrate:**
--* InVID/WeVerify (video verification)
--* Bellingcat toolkit
--* Additional TBD based on V1.0 learnings
--
--=== FR51: Video Verification System ===
--
--**Fulfills:** UN-27 (Visual claims), advanced media verification
--
--**Purpose:** Verify video-based claims.
--
--**Specification:**
--* Keyframe extraction
--* Reverse video search
--* Deepfake detection (AI-powered)
--* Metadata analysis
--* Acoustic signature analysis
--
--=== FR52: Interactive Detection Training ===
--
--**Fulfills:** Media literacy education
--
--**Purpose:** Teach users to identify misinformation.
--
--**Specification:**
--* Interactive tutorials
--* Practice exercises
--* Detection quizzes
--* Gamification elements
--
--=== FR53: Cross-Organizational Sharing ===
--
--**Fulfills:** Collaboration with other fact-checkers
--
--**Purpose:** Share findings with IFCN/EFCSN members.
--
--**Specification:**
--* API for fact-checking organizations
--* Structured data exchange
--* Privacy controls
--* Attribution requirements
--
--== Summary ==
--
--**V1.0 Critical Requirements (Must Have):**
--
--* FR44: ClaimReview Schema ✅
--* FR45: Corrections Notification ✅
--* FR46: Image Verification ✅
--* FR47: Archive.org Integration ✅
--* FR48: Contributor Safety ✅
--* FR49: A/B Testing ✅
--* FR54: Evidence Deduplication ✅
--* NFR11: Quality Assurance Framework ✅
--* NFR12: Security Controls ✅
--* NFR13: Quality Metrics Dashboard ✅
--
--**V1.1+ (Future):**
--
--* FR50: OSINT Integration
--* FR51: Video Verification
--* FR52: Detection Training
--* FR53: Cross-Org Sharing
--
--**Total:** 11 critical requirements for V1.0
--
--=== FR54: Evidence Deduplication ===
--
--**Fulfills:** Accurate evidence counting, quality metrics
--
--**Purpose:** Avoid counting the same source multiple times when it appears in different forms.
--
--**Specification:**
--
--**Deduplication Logic:**
--
--1. **URL Normalization:**
-- * Remove tracking parameters (?utm_source=...)
-- * Normalize http/https
-- * Normalize www/non-www
-- * Handle redirects
--
--2. **Content Similarity:**
-- * If two sources have >90% text similarity → Same source
-- * If one is subset of other → Same source
-- * Use fuzzy matching for minor differences
--
--3. **Cross-Domain Syndication:**
-- * Detect wire service content (AP, Reuters)
-- * Mark as single source if syndicated
-- * Count original publication only
--
--**Display:**
--
--{{code}}
--Evidence Sources (3 unique, 5 total):
--
--1. Original Article (NYTimes)
-- - Also appeared in: WashPost, Guardian (syndicated)
--
--2. Research Paper (Nature)
--
--3. Official Statement (WHO)
--{{/code}}
--
--**Acceptance Criteria:**
--
--* ✅ URL normalization works
--* ✅ Content similarity detected
--* ✅ Syndicated content identified
--* ✅ Unique vs. total counts accurate
--* ✅ Improves evidence quality metrics
--
--== Additional Requirements (Lower Importance) ===== FR7: Automated Verdicts (Enhanced with Quality Gates) ===
--
--**POC1+ Enhancement:**
--
--After AKEL generates verdict, it passes through quality gates:
--
--{{code}}
--Workflow:
--1. Extract claims
-- ↓
--2. [GATE 1] Validate fact-checkable
-- ↓
--3. Generate scenarios
-- ↓
--4. Generate verdicts
-- ↓
--5. [GATE 4] Validate confidence
-- ↓
--6. Display to user
--{{/code}}
--
--**Updated Verdict States:**
--* PUBLISHED
--* INSUFFICIENT_EVIDENCE
--* NON_FACTUAL_CLAIM
--* PROCESSING
--* ERROR
--
--=== FR4: Analysis Summary (Enhanced with Quality Metadata) ===
--
--**POC1+ Enhancement:**
--
--Display quality indicators:
--
--{{code}}
--Analysis Summary:
-- Verifiable Claims: 3/5
-- High Confidence Verdicts: 1
-- Medium Confidence: 2
-- Evidence Sources: 12
-- Avg Source Quality: 0.73
-- Quality Score: 8.5/10
--{{/code}}
--

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